ArticlePloS one2025
Evaluation of imputation strategies for multi-centre studies: Application to a large clinical pathology dataset.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Heterogeneous associations of cumulative heat exposure with cognitive decline among older Japanese adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Time-restricted eating versus calorie restriction for improving biomarkers of age in adults with overweight or obesity and incipient fatty liver disease: protocol for the ENSATI randomized controlled parallel groups trial.Frontiers in endocrinology · 2026Article
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7 authors.
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Abstract
As part of a strategy for accommodating missing data in large heterogeneous datasets, two Random Forest-based (RF) imputation methods, missForest and MICE were evaluated along with several strategies to help navigate the inherently incomplete structure of the dataset.
backgroundA total of 3817 complete cases of clinical chemistry variables from a large-scale, multi-site preclinical longitudinal pathology study were used as an evaluation dataset. Three types of 'missingness' in various proportions were artificially introduced to compare imputation performance for different strategies including variable inclusion and stratification.
resultsMissForest was found to outperform MICE, being robust and capable of automatic variable selection. Stratification had minimal effect on missForest but severely deteriorated the performance of MICE.
conclusionIn general, storing and sharing datasets prior to any correction is a good practise, so that imputation can be performed on merged data if necessary.
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